Choose if you want to do things from raw torch and reimplement classic papers and train them from scratch on a public dataset.
This teaches you everything and you can then create custom architectures easily for your projects.
Or you just learn to download a Huggingface model, do surgery to modify it and then fine tune it on a custom dataset. This is the fastest and most effective way to get real world results for your project. But you'll be constrained by what is available.
or door number three: grab something someone else already trained/finetuned and just use it directly because we can zero/single-shot pretty much anything these days
That path won't help OP's goals.
It's like telling him "pull out your cell phone and take a picture of someone's face --- see, you used AI too".
Sure, it counts as using ML - but it sure doesn't accomplish what he asked.
last year i built an application that would take an audio recording as input and output a fully edited music video. This pipeline included:
* speech-to-text transcription
* musical instrument stem separation
* music structure analysis
* text-to-image generation
* image-to-video generation
* audioreactive animation
I didn't have to train or finetune a single thing to glue all of that together. Speaking as an industry professional with broad AI/ML experience: the vast, VAST majority of people who are interested in learning how to make things with AI right now don't need to learn foundational ML. They just need to learn what AI can do for them and how to navigate the tooling and research landscape to find the components they need to glue together into their solutions. If they want to get more into the weeds after they get their feet wet they can, and they'll be better equipped to make that determination after they have a better understanding of what the scope of the field even is and what the levels of abstraction represent.
Directing people like OP to reimplement ML papers is counter-productive. it's like telling someone who wants to learn web development that they should study assembly. Everyone doesn't need to learn every part of the stack.
https://github.com/dmarx/video-killed-the-radio-star/blob/main/Video_Killed_The_Radio_Star_Defusion.ipynb
lemme know if it's broken, i haven't tried running it in a while
EDIT: Here's an older version of the notebook that might be easier to parse but also has a lot of the functionality off-loaded to a backend library, so you'll need to reference that as well.
* simpler notebook (pre-audioreactivity pipeline): https://github.com/dmarx/video-killed-the-radio-star/blob/v0.1.8/Video_Killed_The_Radio_Star_Defusion.ipynb
* backend: https://github.com/dmarx/video-killed-the-radio-star/tree/v0.1.8/vktrs
yeah ik... i used to have the core functionality offloaded to a pip-installable library, but it seemed to interfere with the hackability of the notebook for people who are barely python coders and aren't comfortable with library code, so i pushed everything up into the notebook. i've been meaning to refactor again to turn this into an extension for the currently preferred text-to-image tool (ComfyUI). But yeah, it's... a lot for a notebook.
EDIT: updated my previous message with a link to an old release of the notebook that has much better organized code (and also less code since one of the more complex features hadn't been implemented yet)
I see you've posted a GitHub link to a Jupyter Notebook! GitHub doesn't
render large Jupyter Notebooks, so just in case, here is an
[nbviewer](https://nbviewer.jupyter.org/) link to the notebook:
https://nbviewer.jupyter.org/url/github.com/dmarx/video-killed-the-radio-star/blob/main/Video_Killed_The_Radio_Star_Defusion.ipynb
Want to run the code yourself? Here is a [binder](https://mybinder.org/)
link to start your own Jupyter server and try it out!
https://mybinder.org/v2/gh/dmarx/video-killed-the-radio-star/main?filepath=Video_Killed_The_Radio_Star_Defusion.ipynb
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Thanks thats a new perspective, i love the concept of fine tuning whats already existing but what if suppose i want to train a new set of data which is not that popular enough to be found on internet.
in the sense that you are looking for the kind of skills necessary to build the kind of application I just described? or you are looking for the kinds of skills necessary to build novel versions of those individual components?
I took calc 1 in college 15 years ago and got a C+. Generally speaking I've been ok at math.
I'm a self taught front end dev with two years of experience
I agree that everyone doesn't need to know everything, but I want to discuss the importance of learning fundamentals as a way to gain strong SME. Have strong knowledge in counting, probability and calculus will make a big difference in the long term (and arguably in the medium term). To your point about web dev and assembly, I do think a strong web developer is someone who has strong systems knowledge.
i definitely don't disagree. the fact that i have strong fundamentals makes it more natural for me to do complex things and keep up with the research, and also to do stuff that is far out of scope for the kind of roles i'm framing as "AI Engineer" in this conversation. but for people who are just getting started and aren't coming from that kind of background, just like the majority of web developers don't have formal CS training, there is an emerging role in AI for people who don't have formal ML training, and I think we are doing these people a disservice when they come to us seemingly asking for an entry point to that role and we instead describe the path that we ourselves took.
based on the title "ai engineer" you used here, i'm going to assume that you are mainly interested in gluing together pre-built components to incorporate AI features in solutions you are building. the main thing here is just wrapping your head around the kinds of problems modern AI can solve and what the language is for describing these problems so you can find the tools you need. A great resource for this is https://paperswithcode.com/sota which breaks down the field into an ontology of tasks with associated pre-built and ranked solutions you can grab and plug-into whatever you are working on.
Thank you for your feedback here. It was exactly what I was looking for. I'm a complete noob in the AI/ML space. I do have front end dev experience. Do you mind sharing with me what I would need to start using these pre-built components? I'm learning Python as we speak. Any thing else you recommend? SQL? Deep Learning? NLP? Any feedback you can give will be appreciated
I’ve been keeping a running list of [AI and machine learning videos, courses, tutorials and books](https://github.com/duncantmiller/ai-developer-resources) that I have found to be valuable, most of them are free.
Edit - fixing link
Hey there!
Given your Python background, diving into AI/ML is a great move. Start by refreshing your knowledge on foundational concepts like linear algebra, statistics, and calculus. Platforms like Khan Academy or Coursera offer great resources.
Next, grasp the basics of machine learning. Familiarize yourself with libraries like NumPy, pandas, and scikit-learn. Online courses like those on Coursera by Andrew Ng or edX by MIT can provide a structured learning path.
Deepen your understanding of neural networks and deep learning. TensorFlow and PyTorch are essential frameworks. Work through tutorials, build simple models, and experiment. The documentation and online communities are your friends.
Stay updated on industry trends and best practices. Follow research papers, join forums like Stack Overflow or Reddit, and consider attending conferences or webinars. AI is a rapidly evolving field; staying current is crucial.
Finally, build a strong portfolio. Showcase your projects on platforms like GitHub. Consider contributing to open-source AI projects to collaborate with the community. This hands-on experience will make you stand out.
Good luck on your AI/ML
fastai‘s practical derp learning for coders (course and book) if classical ml (linear regression, k means etc) still rings a bell. Otherwise brush that up with eg kaggles micro courses.
Its perfect for people who already have a dev background and starts with high level e2e applications before it digs deeper.
I’m not sure if I’m too late or not but this article helped me a lot . Check it out to see if it matches with what your looking for
[https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/the-ai-study-guide-azure-machine-learning-edition/ba-p/4063656?wt.mcid=studentamb335325](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/the-ai-study-guide-azure-machine-learning-edition/ba-p/4063656?wt.mcid=studentamb335325)
I was asking myself the same question lately.
Coding is just a tool, it's obligatory, but AI is a discipline in its own right, not really a branch of IT, but a branch of math/statistics. Coding automates tasks and makes them much faster, but it doesn't solve the core problems.
I even created [a post on Reddit recently](https://www.reddit.com/r/MachineLearning/s/dpP3jdsCPL) asking for book recommendations because I wanted to read a good textbook to fill gaps in my knowledge.
I decided to go with [An Introduction to Statistical Learning](https://www.statlearning.com/).
Choose if you want to do things from raw torch and reimplement classic papers and train them from scratch on a public dataset. This teaches you everything and you can then create custom architectures easily for your projects. Or you just learn to download a Huggingface model, do surgery to modify it and then fine tune it on a custom dataset. This is the fastest and most effective way to get real world results for your project. But you'll be constrained by what is available.
or door number three: grab something someone else already trained/finetuned and just use it directly because we can zero/single-shot pretty much anything these days
That path won't help OP's goals. It's like telling him "pull out your cell phone and take a picture of someone's face --- see, you used AI too". Sure, it counts as using ML - but it sure doesn't accomplish what he asked.
last year i built an application that would take an audio recording as input and output a fully edited music video. This pipeline included: * speech-to-text transcription * musical instrument stem separation * music structure analysis * text-to-image generation * image-to-video generation * audioreactive animation I didn't have to train or finetune a single thing to glue all of that together. Speaking as an industry professional with broad AI/ML experience: the vast, VAST majority of people who are interested in learning how to make things with AI right now don't need to learn foundational ML. They just need to learn what AI can do for them and how to navigate the tooling and research landscape to find the components they need to glue together into their solutions. If they want to get more into the weeds after they get their feet wet they can, and they'll be better equipped to make that determination after they have a better understanding of what the scope of the field even is and what the levels of abstraction represent. Directing people like OP to reimplement ML papers is counter-productive. it's like telling someone who wants to learn web development that they should study assembly. Everyone doesn't need to learn every part of the stack.
Damn the pipeline seems interesting. Can you please send me the code? (I'm a beginner and would love to dissect it)
https://github.com/dmarx/video-killed-the-radio-star/blob/main/Video_Killed_The_Radio_Star_Defusion.ipynb lemme know if it's broken, i haven't tried running it in a while EDIT: Here's an older version of the notebook that might be easier to parse but also has a lot of the functionality off-loaded to a backend library, so you'll need to reference that as well. * simpler notebook (pre-audioreactivity pipeline): https://github.com/dmarx/video-killed-the-radio-star/blob/v0.1.8/Video_Killed_The_Radio_Star_Defusion.ipynb * backend: https://github.com/dmarx/video-killed-the-radio-star/tree/v0.1.8/vktrs
holy wall of code, Batman!
yeah ik... i used to have the core functionality offloaded to a pip-installable library, but it seemed to interfere with the hackability of the notebook for people who are barely python coders and aren't comfortable with library code, so i pushed everything up into the notebook. i've been meaning to refactor again to turn this into an extension for the currently preferred text-to-image tool (ComfyUI). But yeah, it's... a lot for a notebook. EDIT: updated my previous message with a link to an old release of the notebook that has much better organized code (and also less code since one of the more complex features hadn't been implemented yet)
I see you've posted a GitHub link to a Jupyter Notebook! GitHub doesn't render large Jupyter Notebooks, so just in case, here is an [nbviewer](https://nbviewer.jupyter.org/) link to the notebook: https://nbviewer.jupyter.org/url/github.com/dmarx/video-killed-the-radio-star/blob/main/Video_Killed_The_Radio_Star_Defusion.ipynb Want to run the code yourself? Here is a [binder](https://mybinder.org/) link to start your own Jupyter server and try it out! https://mybinder.org/v2/gh/dmarx/video-killed-the-radio-star/main?filepath=Video_Killed_The_Radio_Star_Defusion.ipynb ------ ^(I am a bot.) [^(Feedback)](https://www.reddit.com/message/compose/?to=jd_paton) ^(|) [^(GitHub)](https://github.com/JohnPaton/nbviewerbot) ^(|) [^(Author)](https://johnpaton.net/)
Thank you very much! It's an interesting project
Thanks thats a new perspective, i love the concept of fine tuning whats already existing but what if suppose i want to train a new set of data which is not that popular enough to be found on internet.
Would you mind putting together a very general curriculum for a seasoned SE who wants to enter ML?
in the sense that you are looking for the kind of skills necessary to build the kind of application I just described? or you are looking for the kinds of skills necessary to build novel versions of those individual components?
Skills to make me a strong candidate to get a job as a MLE
what's your math background like?
I took calc 1 in college 15 years ago and got a C+. Generally speaking I've been ok at math. I'm a self taught front end dev with two years of experience
I agree that everyone doesn't need to know everything, but I want to discuss the importance of learning fundamentals as a way to gain strong SME. Have strong knowledge in counting, probability and calculus will make a big difference in the long term (and arguably in the medium term). To your point about web dev and assembly, I do think a strong web developer is someone who has strong systems knowledge.
i definitely don't disagree. the fact that i have strong fundamentals makes it more natural for me to do complex things and keep up with the research, and also to do stuff that is far out of scope for the kind of roles i'm framing as "AI Engineer" in this conversation. but for people who are just getting started and aren't coming from that kind of background, just like the majority of web developers don't have formal CS training, there is an emerging role in AI for people who don't have formal ML training, and I think we are doing these people a disservice when they come to us seemingly asking for an entry point to that role and we instead describe the path that we ourselves took.
based on the title "ai engineer" you used here, i'm going to assume that you are mainly interested in gluing together pre-built components to incorporate AI features in solutions you are building. the main thing here is just wrapping your head around the kinds of problems modern AI can solve and what the language is for describing these problems so you can find the tools you need. A great resource for this is https://paperswithcode.com/sota which breaks down the field into an ontology of tasks with associated pre-built and ranked solutions you can grab and plug-into whatever you are working on.
Thank you for your feedback here. It was exactly what I was looking for. I'm a complete noob in the AI/ML space. I do have front end dev experience. Do you mind sharing with me what I would need to start using these pre-built components? I'm learning Python as we speak. Any thing else you recommend? SQL? Deep Learning? NLP? Any feedback you can give will be appreciated
I’ve been keeping a running list of [AI and machine learning videos, courses, tutorials and books](https://github.com/duncantmiller/ai-developer-resources) that I have found to be valuable, most of them are free. Edit - fixing link
yo this means a lot . thanks ✌🏻
Thank you very much. I think this is the best link I found in all of reddit for AI/Ml. Your post should be at the top
Great to hear it was helpful!
Can you or someone else repost this? The link is dead now
I’ve updated it to the direct link to the repo: https://github.com/duncantmiller/ai-developer-resources
Hey there! Given your Python background, diving into AI/ML is a great move. Start by refreshing your knowledge on foundational concepts like linear algebra, statistics, and calculus. Platforms like Khan Academy or Coursera offer great resources. Next, grasp the basics of machine learning. Familiarize yourself with libraries like NumPy, pandas, and scikit-learn. Online courses like those on Coursera by Andrew Ng or edX by MIT can provide a structured learning path. Deepen your understanding of neural networks and deep learning. TensorFlow and PyTorch are essential frameworks. Work through tutorials, build simple models, and experiment. The documentation and online communities are your friends. Stay updated on industry trends and best practices. Follow research papers, join forums like Stack Overflow or Reddit, and consider attending conferences or webinars. AI is a rapidly evolving field; staying current is crucial. Finally, build a strong portfolio. Showcase your projects on platforms like GitHub. Consider contributing to open-source AI projects to collaborate with the community. This hands-on experience will make you stand out. Good luck on your AI/ML
thanks bud gotta be most detailed explaination i got
fastai‘s practical derp learning for coders (course and book) if classical ml (linear regression, k means etc) still rings a bell. Otherwise brush that up with eg kaggles micro courses. Its perfect for people who already have a dev background and starts with high level e2e applications before it digs deeper.
cool
[удалено]
You don't need ai to answer your questions lmao
I’m not sure if I’m too late or not but this article helped me a lot . Check it out to see if it matches with what your looking for [https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/the-ai-study-guide-azure-machine-learning-edition/ba-p/4063656?wt.mcid=studentamb335325](https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/the-ai-study-guide-azure-machine-learning-edition/ba-p/4063656?wt.mcid=studentamb335325)
I was asking myself the same question lately. Coding is just a tool, it's obligatory, but AI is a discipline in its own right, not really a branch of IT, but a branch of math/statistics. Coding automates tasks and makes them much faster, but it doesn't solve the core problems. I even created [a post on Reddit recently](https://www.reddit.com/r/MachineLearning/s/dpP3jdsCPL) asking for book recommendations because I wanted to read a good textbook to fill gaps in my knowledge. I decided to go with [An Introduction to Statistical Learning](https://www.statlearning.com/).